Information Theory and Coding

Related lectures (30)

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Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Covers the Source Coding Theorem, source models, entropy, regular sources, and examples.

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Explores mutual information and entropy calculation between random variables.

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Discusses conditional entropy and its role in data compression techniques.

Covers the demonstration of Shannon's theorem, focusing on data compression.

Covers the Lempel-Ziv universal coding algorithm and invertible finite state machines in information theory.

Discusses conditional entropy and its role in information theory and data compression.